def test_safe_compute_error():
A = np.abs(random_state.randn(10, 10))
A[:, 2 * np.arange(5)] = 0
A_sparse = csc_matrix(A)
W, H = nmf._initialize_nmf(A, 5, init='random', random_state=0)
error = nmf._safe_compute_error(A, W, H)
error_sparse = nmf._safe_compute_error(A_sparse, W, H)
assert_almost_equal(error, error_sparse)
def test_safe_compute_error():
A = np.abs(random_state.randn(10, 10))
A[:, 2 * np.arange(5)] = 0
A_sparse = csc_matrix(A)
W, H = nmf._initialize_nmf(A, 5, init='random', random_state=0)
error = nmf._safe_compute_error(A, W, H)
error_sparse = nmf._safe_compute_error(A_sparse, W, H)
assert_almost_equal(error, error_sparse)
def test_sparse_input():
# Test that sparse matrices are accepted as input
from scipy.sparse import csc_matrix
A = np.abs(random_state.randn(10, 10))
A[:, 2 * np.arange(5)] = 0
A_sparse = csc_matrix(A)
for solver in ('proj-grad', 'coordinate', 'greedy'):
est1 = nmf.NMF(solver=solver,
n_components=5,
init='random',
random_state=0,
tol=1e-2)
est2 = clone(est1)
W1 = est1.fit_transform(A)
W2 = est2.fit_transform(A_sparse)
H1 = est1.components_
H2 = est2.components_
assert_array_almost_equal(est2.reconstruction_err_,
nmf._safe_compute_error(A, W2, H2))
assert_array_almost_equal(W1, W2)
assert_array_almost_equal(H1, H2)